Dokładność nowego hybrydowego uogólnionego modelu falkowo-sieciowego z długą 
pamięcią do krótkoterminowego prognozowania cen energii elektrycznej
[Predictive accuracy of a new hybrid generalized long memory wavelet-neural 
networks model for short term electricity price forecasting]

Souhir Ben Amor 
(High Institute of Commercial Studies of Sousse, IHEC, Tunisia)


Accurate electricity price forecasting is the main management goal for market 
participants since it represents the fundamental basis to maximize the profits 
for market players. However, electricity is a non-storable commodity and the 
electricity prices are affected by some social and natural factors that make the 
price forecasting a challenging task. This study investigates the predictive 
performance of a new hybrid model based on Generalized long memory autoregressive 
model (k-factor GARMA), the Gegenbauer Generalized Autoregressive Conditional 
Heteroscedasticity(G-GARCH) process, Wavelet decomposition, and Local Linear 
Wavelet Neural Network (LLWNN) optimized using two different learning algorithms; 
the Back propagation algorithm (BP) and the Particle Swarm optimization algorithm 
(PSO). The performance of the proposed model is evaluated using data from Nord 
Pool Electricity markets. Moreover, it is compared with some other parametric and 
non-parametric models in order to prove its robustness. The empirical results 
prove that the proposed method performs better than other competing techniques.

(*) Based on joint work with Heni Boubaker and Lotfi Belkacem (High Institute 
of Commercial Studies of Sousse, IHEC).